Musall, S., Urai, A. E., Sussillo, D., Churchland, A. K. (October 2019) Harnessing behavioral diversity to understand neural computations for cognition. Curr Opin Neurobiol, 58. pp. 229-238. ISSN 0959-4388
Abstract
With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics organism description > animal behavior organism description > animal behavior > perception > cognition bioinformatics > computational biology organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks |
| CSHL Authors: | |
| Communities: | CSHL labs > Churchland lab |
| Depositing User: | Matthew Dunn |
| Date: | 25 October 2019 |
| Date Deposited: | 08 Nov 2019 16:51 |
| Last Modified: | 02 Feb 2024 19:28 |
| PMCID: | PMC6931281 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/38675 |
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